6 papers
Neuro-Symbolic Learning for Long-Horizon Task Planning Under Complex Logical Constraints
Qiwei Du, Zitong Zhan, Shaoshu Su +7
Task planning often suffers from severe efficiency bottlenecks when robots must reason over long-horizon action sequences under complex logical constraints, including object afford…
What Matters When Cotraining Robot Manipulation Policies on Everyday Human Videos?
Richard Li, Aditya Prakash, Andrew Wen +3
Human video datasets used for cotraining robot manipulation policies largely consist of curated demonstrations where motions are orchestrated to resemble robot behavior and 3D hand…
World Action Verifier: Self-Improving World Models via Forward-Inverse Asymmetry
Yuejiang Liu, Fan Feng, Lingjing Kong +6
General-purpose world models promise scalable policy evaluation, optimization, and planning, yet achieving the required level of robustness remains challenging. Unlike policy learn…
Inference-Time Enhancement of Generative Robot Policies via Predictive World Modeling
Han Qi, Haocheng Yin, Aris Zhu +2
We present Generative Predictive Control (GPC), an inference-time method for improving pretrained behavior-cloning policies without retraining. GPC augments a frozen diffusion poli…
Scaling Tasks, Not Samples: Mastering Humanoid Control through Multi-Task Model-Based Reinforcement Learning
Shaohuai Liu, Weirui Ye, Yilun Du +1
Developing generalist robots capable of mastering diverse skills remains a central challenge in embodied AI. While recent progress emphasizes scaling model parameters and offline d…
Towards Generalist Robot Learning from Internet Video: A Survey
Robert McCarthy, Daniel C. H. Tan, Dominik Schmidt +5
Scaling deep learning to massive and diverse internet data has driven remarkable breakthroughs in domains such as video generation and natural language processing. Robot learning,…